Multipath Routing
Content
MULTIPLE PATHS ROUTING USING PORTFOLIO SELECTION
M.Ganesh Kumar
II-ME/CSE
Mrs.S. SHOBANA, M.E.,
Assistant. Professor
Jayam College of Engineering and Technology Dharmapuri,Tamilnadu. [email protected] M.No:9994331160
Abstract— Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. Consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. Formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics.This centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). The network’s ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation and congestion avoidance is to avoid delay and maximize throughput. Index Terms—Jamming, Multiple path routing, Portfolio selection theory, Optimization, Network utility maximization
Dept. of Computer Science and Engineering Jayam College of Engineering and Technology Dharmapuri,Tamilnadu.
example the MPDSR protocol, each source node can request several routing paths to the destination node for concurrent use. To make effective use of this routing diversity, however, each Source node must be able to make an intelligent allocation of traffic across the available paths while considering the potential effect of jamming on the resulting data throughput.
INTRODUCTION Jamming point-to-point transmissions in a wireless mesh network or underwater acoustic network can have debilitating effects on data transport through the network. The effects of jamming at the physical layer resonate through the protocol stack, providing an effective denial-of-service (DoS) attack on end-to-end data communication. The simplest methods to defend a network against jamming attacks comprise physical layer solutions such as spread-spectrum or beamforming, forcing the jammers to expend a greater resource to reach the same goal. However, recent work has demonstrated that intelligent jammers can incorporate crosslayer protocol information into jamming attacks, reducing resource expenditure by several orders of magnitude by targeting certain link layer and MAC implementations as well as link layer error detection and correction protocols. Hence, more sophisticated anti-jamming methods and defensive measures must be incorporated into higher-layer protocols, for example channel surfing or routing around jammed regions of the network. The majority of anti-jamming techniques make use of diversity. anti-jamming protocols may employ multiple frequency bands, differentMAC channels, or multiple routing paths. Such diversity techniques help to curb the effects of the jamming attack by requiring the jammer to act on multiple resources simultaneously. Using multiple-path variants of source routing protocols such as Dynamic Source Routing (DSR) or Ad-Hoc On-Demand Distance Vector (AODV) , for
The ability of network nodes to characterize the jamming impact and the ability of multiple source nodes to compensate for jamming.
The allocation of traffic across multiple routing paths. Our contributions to this problem are as follow: 1.Formulate the problem of allocating traffic across multiple routing paths in the presence of jamming as a lossy network flow optimization problem. We map the optimization
problem to that of asset allocation using portfolio selection theory. 2.Formulate the centralized traffic allocation problem for multiple source nodes as a convex optimization problem. 3.Show that the multisource multiple-path optimal traffic allocation can be computed at the source nodes using a distributed algorithm based on decomposition in network utility maximization 4. Propose methods which allow individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes. 5. Demonstrate that the use of portfolio selection theory allows the data sources to balance the expected data throughput with the uncertainty in achievable traffic rates. 1.ALLOCATION OF TRAFFIC ACROSS MULTIPLE ROUTING PATHS We formulate the problem of allocating traffic across multiple routing paths in the presence of jamming as a lossy network flow optimization problem. We map the optimization problem to that of asset allocation using portfolio selection theory which allows individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes. 2. CHARACTERIZING THE IMPACT OF JAMMING In these Module the network nodes to estimate and characterize the impact of jamming and for a source node to incorporate these estimates into its traffic allocation. In order for a source node s to incorporate the jamming impact in the traffic allocation problem, the effect of jamming on transmissions over each link must be estimated. However, to capture the jammer mobility and the dynamic effects of the jamming attack, the local estimates need to be continually updated.
The capacity indicating the link maximum number of packets persecond (pkt/s) eg:200 pkts/s which can be transported over the wireless link. Whenever the source is generating data at a rate of 300 pkts/s to be transmitted at the time jamming to be occurring. Then the throughput rate to be less. If the source node becomes aware of this effect the allocation of traffic can be changed to 150 pkts/s on each of paths thus recovers the jamming path. 4.ESTIMATING RATES The packet success rate estimates for the links in a routing path, the source needs to estimate the effective end-to-end packet success rate to determine the optimal traffic allocation. Assuming the total time required to transport packets from each source s to the corresponding destination is negligible compared to the update relay period. 5.OPTIMAL JAMMINGAWARE TRAFFIC ALLOCATION An optimization framework for jamming-aware traffic allocation to multiple routing paths for each source node. We develop a set of constraints imposed on traffic allocation solutions and then formulate a utility function for optimal traffic allocation by mapping the problem to that of portfolio selection in finance. A. Optimal Distributed Traffic Allocation using NUM In the distributed formulation of the algorithm, each source s determines its own traffic allocation "s, ideallywithminimal message passing between sources. By inspection, we see that the optimal jamming-aware flow allocation problem in is similar to the network utility maximization (NUM) formulation of the basic maximum network flow problem [14]. We thus develop a distributed traffic allocation algorithm using Lagrangian dual decomposition techniques for NUM. B. Optimal Traffic Allocation Using Portfolio Selection Theory In Markowitz’s portfolio selection theory [12], [13], an investor is interested in allocating funds to a set of financial assets that have uncertain future performance. The expected performance of each investment at the time of the initial allocation is expressed in terms of return and risk. The return on the asset corresponds to the value of the asset and measures the growth of the investment. The risk of the asset corresponds to the variance in the value of the asset and measures the degree of variation or uncertainty in the investment’s growth END-to-END PACKET SUCCESS
3.EFFECT OF JAMMER IN MOBILITY NETWORK
VI. CONCLUSION
In this article, we studied the problem of traffic allocation in multiple-path routing algorithms in the presence of jammers whose effect can only be characterized statistically. We have presented methods for each network node to probabilistically characterize the local impact of a dynamic jamming attack and for data sources to incorporate this information into the routing algorithm. We formulated multiple-path traffic allocation in multi-source networks as a lossy network flow optimization problem using an objective function based on portfolio selection theory from finance. We showed that this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We presented simulation results to illustrate the impact of jamming dynamics and mobility on network throughput and to demonstrate the efficacy of our traffic allocation algorithm. We have thus shown that multiplepath source routing algorithms can optimize the throughput performance by effectively incorporating the empirical jamming impact into the allocation of traffic to the set of paths. REFERENCES
[1] I. F. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: A survey,” Computer Networks, vol. 47, no. 4, pp. 445–487, Mar. 2005. [2] E. M. Sozer, M. Stojanovic, and J. G. Proakis, “Underwater acoustic networks,” IEEE Journal of Oceanic Engineering, vol. 25, no. 1, pp. 72–83, Jan. 2000. [3] R. Anderson, Security Engineering: A Guide to Building Dependable Distributed Systems. JohnWiley&Sons, Inc.,2001. [4] J. Bellardo and S. Savage, “802.11 denial-of-service attacks: Real vulnerabilities and practical solutions,” in Proc. USENIX Security Symposium, Washington, DC, Aug. 2003, pp. 15–28. [5] D. J. Thuente and M. Acharya, “Intelligent jamming in wireless networks with applications to 802.11b and other networks,” in Proc. 25th IEEE Communications Society Military Communications Conference (MILCOM’06), Washington, DC, Oct. 2006, pp. 1–7. [6] A. D. Wood and J. A. Stankovic, “Denial of service in sensor networks,” IEEE Computer, vol. 35, no. 10, pp. 54–62, Oct. 2002. [7] G. Lin and G. Noubir, “On link layer denial of service in data wireless LANs,” Wireless Communications and Mobile Computing, vol. 5, no. 3, pp. 273–284, May 2005. [8] W. Xu, K. Ma, W. Trappe, and Y. Zhang, “Jamming sensor networks: Attack and defense strategies,” IEEE Network, vol. 20, no. 3, pp. 41–47, May/Jun. 2006. [9] D. B. Johnson, D. A. Maltz, and J. Broch, DSR: The Dynamic Source Routing Protocol for Multihop Wireless Ad Hoc Networks. AddisonWesley, 2001, ch. 5, pp. 139–172.
[10] E. M. Royer and C. E. Perkins, “Ad hoc on-demand distance vector routing,” in Proc. 2nd IEEE Workshop on mobile Computing Systems and Applications (WMCSA’99), New Orleans, LA, USA, Feb. 1999, pp. 90–100. [11] R. Leung, J. Liu, E. Poon, A.-L. C. Chan, and B. Li, “MPDSR: A QoSaware multi-path dynamic source routing protocol for wireless adhoc networks,” in Proc. 26th Annual IEEE Conference on Local Computer Networks (LCN’01), Tampa, FL, USA, Nov. 2001, pp. 132–141. [12] H. Markowitz, “Portfolio selection,” The Journal of Finance, vol. 7, no. 1, pp. 77–92, Mar. 1952. [13] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge,2004. [14] D. P. Palomar and M. Chiang, “A tutorial on decomposition methods for network utility maximization,” IEEE Journal on Selected Areas in Communications, vol. 24, no. 8, pp. 1439–1451, Aug. 2006. [15] M. Evans, N. Hastings, and B. Peacock, Statistical Distributions, 3rd ed. New York: John Wiley & Sons, Inc., 2000. [16] S. W. Roberts, “Control chart tests based on geometric moving averages,” Technometrics, vol. 42, no. 1, pp. 97–101, Feb. 2000. [17] V. Paxson and M. Allman, “Computing TCP’s retransmission timer,” RFC 2988, Nov. 2000, http://www.ietf.org/rfc/rfc2988.txt. [18] I. R. James, “Products of independent beta variables with applications to connor and mosimann’s generalized dirichlet distribution,” Journal of the American Statistical Association, vol. 67, no. 340, pp. 910–912, Dec. 1972. [19] W. F. Sharpe, Investors and Markets: Portfolio Choices, Asset Prices, and Investment Advice. PrincetonUniversityPress,2007.
M.Ganesh Kumar
II-ME/CSE
Mrs.S. SHOBANA, M.E.,
Assistant. Professor
Jayam College of Engineering and Technology Dharmapuri,Tamilnadu. [email protected] M.No:9994331160
Abstract— Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. Consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. Formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics.This centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). The network’s ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation and congestion avoidance is to avoid delay and maximize throughput. Index Terms—Jamming, Multiple path routing, Portfolio selection theory, Optimization, Network utility maximization
Dept. of Computer Science and Engineering Jayam College of Engineering and Technology Dharmapuri,Tamilnadu.
example the MPDSR protocol, each source node can request several routing paths to the destination node for concurrent use. To make effective use of this routing diversity, however, each Source node must be able to make an intelligent allocation of traffic across the available paths while considering the potential effect of jamming on the resulting data throughput.
INTRODUCTION Jamming point-to-point transmissions in a wireless mesh network or underwater acoustic network can have debilitating effects on data transport through the network. The effects of jamming at the physical layer resonate through the protocol stack, providing an effective denial-of-service (DoS) attack on end-to-end data communication. The simplest methods to defend a network against jamming attacks comprise physical layer solutions such as spread-spectrum or beamforming, forcing the jammers to expend a greater resource to reach the same goal. However, recent work has demonstrated that intelligent jammers can incorporate crosslayer protocol information into jamming attacks, reducing resource expenditure by several orders of magnitude by targeting certain link layer and MAC implementations as well as link layer error detection and correction protocols. Hence, more sophisticated anti-jamming methods and defensive measures must be incorporated into higher-layer protocols, for example channel surfing or routing around jammed regions of the network. The majority of anti-jamming techniques make use of diversity. anti-jamming protocols may employ multiple frequency bands, differentMAC channels, or multiple routing paths. Such diversity techniques help to curb the effects of the jamming attack by requiring the jammer to act on multiple resources simultaneously. Using multiple-path variants of source routing protocols such as Dynamic Source Routing (DSR) or Ad-Hoc On-Demand Distance Vector (AODV) , for
The ability of network nodes to characterize the jamming impact and the ability of multiple source nodes to compensate for jamming.
The allocation of traffic across multiple routing paths. Our contributions to this problem are as follow: 1.Formulate the problem of allocating traffic across multiple routing paths in the presence of jamming as a lossy network flow optimization problem. We map the optimization
problem to that of asset allocation using portfolio selection theory. 2.Formulate the centralized traffic allocation problem for multiple source nodes as a convex optimization problem. 3.Show that the multisource multiple-path optimal traffic allocation can be computed at the source nodes using a distributed algorithm based on decomposition in network utility maximization 4. Propose methods which allow individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes. 5. Demonstrate that the use of portfolio selection theory allows the data sources to balance the expected data throughput with the uncertainty in achievable traffic rates. 1.ALLOCATION OF TRAFFIC ACROSS MULTIPLE ROUTING PATHS We formulate the problem of allocating traffic across multiple routing paths in the presence of jamming as a lossy network flow optimization problem. We map the optimization problem to that of asset allocation using portfolio selection theory which allows individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes. 2. CHARACTERIZING THE IMPACT OF JAMMING In these Module the network nodes to estimate and characterize the impact of jamming and for a source node to incorporate these estimates into its traffic allocation. In order for a source node s to incorporate the jamming impact in the traffic allocation problem, the effect of jamming on transmissions over each link must be estimated. However, to capture the jammer mobility and the dynamic effects of the jamming attack, the local estimates need to be continually updated.
The capacity indicating the link maximum number of packets persecond (pkt/s) eg:200 pkts/s which can be transported over the wireless link. Whenever the source is generating data at a rate of 300 pkts/s to be transmitted at the time jamming to be occurring. Then the throughput rate to be less. If the source node becomes aware of this effect the allocation of traffic can be changed to 150 pkts/s on each of paths thus recovers the jamming path. 4.ESTIMATING RATES The packet success rate estimates for the links in a routing path, the source needs to estimate the effective end-to-end packet success rate to determine the optimal traffic allocation. Assuming the total time required to transport packets from each source s to the corresponding destination is negligible compared to the update relay period. 5.OPTIMAL JAMMINGAWARE TRAFFIC ALLOCATION An optimization framework for jamming-aware traffic allocation to multiple routing paths for each source node. We develop a set of constraints imposed on traffic allocation solutions and then formulate a utility function for optimal traffic allocation by mapping the problem to that of portfolio selection in finance. A. Optimal Distributed Traffic Allocation using NUM In the distributed formulation of the algorithm, each source s determines its own traffic allocation "s, ideallywithminimal message passing between sources. By inspection, we see that the optimal jamming-aware flow allocation problem in is similar to the network utility maximization (NUM) formulation of the basic maximum network flow problem [14]. We thus develop a distributed traffic allocation algorithm using Lagrangian dual decomposition techniques for NUM. B. Optimal Traffic Allocation Using Portfolio Selection Theory In Markowitz’s portfolio selection theory [12], [13], an investor is interested in allocating funds to a set of financial assets that have uncertain future performance. The expected performance of each investment at the time of the initial allocation is expressed in terms of return and risk. The return on the asset corresponds to the value of the asset and measures the growth of the investment. The risk of the asset corresponds to the variance in the value of the asset and measures the degree of variation or uncertainty in the investment’s growth END-to-END PACKET SUCCESS
3.EFFECT OF JAMMER IN MOBILITY NETWORK
VI. CONCLUSION
In this article, we studied the problem of traffic allocation in multiple-path routing algorithms in the presence of jammers whose effect can only be characterized statistically. We have presented methods for each network node to probabilistically characterize the local impact of a dynamic jamming attack and for data sources to incorporate this information into the routing algorithm. We formulated multiple-path traffic allocation in multi-source networks as a lossy network flow optimization problem using an objective function based on portfolio selection theory from finance. We showed that this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We presented simulation results to illustrate the impact of jamming dynamics and mobility on network throughput and to demonstrate the efficacy of our traffic allocation algorithm. We have thus shown that multiplepath source routing algorithms can optimize the throughput performance by effectively incorporating the empirical jamming impact into the allocation of traffic to the set of paths. REFERENCES
[1] I. F. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: A survey,” Computer Networks, vol. 47, no. 4, pp. 445–487, Mar. 2005. [2] E. M. Sozer, M. Stojanovic, and J. G. Proakis, “Underwater acoustic networks,” IEEE Journal of Oceanic Engineering, vol. 25, no. 1, pp. 72–83, Jan. 2000. [3] R. Anderson, Security Engineering: A Guide to Building Dependable Distributed Systems. JohnWiley&Sons, Inc.,2001. [4] J. Bellardo and S. Savage, “802.11 denial-of-service attacks: Real vulnerabilities and practical solutions,” in Proc. USENIX Security Symposium, Washington, DC, Aug. 2003, pp. 15–28. [5] D. J. Thuente and M. Acharya, “Intelligent jamming in wireless networks with applications to 802.11b and other networks,” in Proc. 25th IEEE Communications Society Military Communications Conference (MILCOM’06), Washington, DC, Oct. 2006, pp. 1–7. [6] A. D. Wood and J. A. Stankovic, “Denial of service in sensor networks,” IEEE Computer, vol. 35, no. 10, pp. 54–62, Oct. 2002. [7] G. Lin and G. Noubir, “On link layer denial of service in data wireless LANs,” Wireless Communications and Mobile Computing, vol. 5, no. 3, pp. 273–284, May 2005. [8] W. Xu, K. Ma, W. Trappe, and Y. Zhang, “Jamming sensor networks: Attack and defense strategies,” IEEE Network, vol. 20, no. 3, pp. 41–47, May/Jun. 2006. [9] D. B. Johnson, D. A. Maltz, and J. Broch, DSR: The Dynamic Source Routing Protocol for Multihop Wireless Ad Hoc Networks. AddisonWesley, 2001, ch. 5, pp. 139–172.
[10] E. M. Royer and C. E. Perkins, “Ad hoc on-demand distance vector routing,” in Proc. 2nd IEEE Workshop on mobile Computing Systems and Applications (WMCSA’99), New Orleans, LA, USA, Feb. 1999, pp. 90–100. [11] R. Leung, J. Liu, E. Poon, A.-L. C. Chan, and B. Li, “MPDSR: A QoSaware multi-path dynamic source routing protocol for wireless adhoc networks,” in Proc. 26th Annual IEEE Conference on Local Computer Networks (LCN’01), Tampa, FL, USA, Nov. 2001, pp. 132–141. [12] H. Markowitz, “Portfolio selection,” The Journal of Finance, vol. 7, no. 1, pp. 77–92, Mar. 1952. [13] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge,2004. [14] D. P. Palomar and M. Chiang, “A tutorial on decomposition methods for network utility maximization,” IEEE Journal on Selected Areas in Communications, vol. 24, no. 8, pp. 1439–1451, Aug. 2006. [15] M. Evans, N. Hastings, and B. Peacock, Statistical Distributions, 3rd ed. New York: John Wiley & Sons, Inc., 2000. [16] S. W. Roberts, “Control chart tests based on geometric moving averages,” Technometrics, vol. 42, no. 1, pp. 97–101, Feb. 2000. [17] V. Paxson and M. Allman, “Computing TCP’s retransmission timer,” RFC 2988, Nov. 2000, http://www.ietf.org/rfc/rfc2988.txt. [18] I. R. James, “Products of independent beta variables with applications to connor and mosimann’s generalized dirichlet distribution,” Journal of the American Statistical Association, vol. 67, no. 340, pp. 910–912, Dec. 1972. [19] W. F. Sharpe, Investors and Markets: Portfolio Choices, Asset Prices, and Investment Advice. PrincetonUniversityPress,2007.
